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SASInstitute Modeling Using SAS Visual Statistics Sample Questions:

1. Refer to the exhibit from a linear regression model in SAS Visual Statistics:

Based on the table above and assuming a significance level of 0.05, what can be concluded about the linear regression model?

A) For one one-unit increase in Average Sales, there is an expected increase in the response of
4475.443.
B) For a 1371.5 unit decrease in Total Promos, there is an expected one-unit increase in the response.
C) Age is a significant predictor of the response.
D) The Intercept is an important predictor of the response.

2. In linear regression modeling, what roles are typically assigned to variables?

A) Dependent, Independent, and Categorical
B) Predictor, Response, and Interaction
C) Input, Target, and Nominal
D) Intercept, Slope, and Error

3. When reviewing Measure Details in SAS Visual Analytics, what information can you find related to measures?

A) Measure name and label
B) Measure calculation method
C) Measure format and informat
D) Measure data type

4. Refer to the fit summary from SAS Visual Statisitcs in the exhibit below.

What can be concluded from the fit summary?

A) Average Sales is an important predictor when Customer Value Level = C.
B) Customer Value Level C has no important variables associated with it.
C) Customer Value Level is not a significant predictor in this model.
D) Average Sales is a significant predictor when Customer Value Level = E.

5. What is one advantage of using SAS Studio to score new data with exported score code?

A) It allows for direct integration with Hadoop clusters.
B) It provides advanced visualization capabilities.
C) It requires no additional software installation.
D) It automatically optimizes the scoring process.

Solutions: